Was Sandmo Right? Experimental Evidence on Attitudes to Price Risk and Uncertainty
Bibliographic record
Abstract
In his seminal 1971 article, Sandmo showed that when faced with an uncertain output price, a risk-averse firm manager would hedge by producing less than he would have when faced with a certain output price. We take Sandmo’s prediction, among other things, to the lab. We study in turn the effects of price risk (i.e., uncertain prices whose distribution is known) and price ambiguity (i.e., uncertain prices whose distribution is not known, but whose range is known) while controlling for our subjects’ income risk preferences. Our experimental protocol closely mimics Sandmo’s theoretical model. For price risk, we use a two-stage randomization strategy aimed first at studying the effect of price uncertainty relative to price certainty, and then the effect of increases in price uncertainty conditional on there being price uncertainty. For price ambiguity, we use the same randomization strategy to study the effect of price ambiguity relative to price certainty while preventing our subjects from guessing the shape of the price distribution. For price risk, we find that, in stark contradiction to Sandmo’s theoretical result, the presence of price uncertainty causes subjects to produce more than they do under price certainty, but that increases in price uncertainty makes them decrease their production monotonically. For price ambiguity, results are mixed and depend on whether the portion of the experiment aimed at eliciting our subjects’ income risk aversion is played before or after the price uncertainty game. Lastly, we use our price risk data to study the problem structurally, in order to get at preference heterogeneity, and find that our structural results are consistent with our reduced-form results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".